[Feature Request] Auto reset between gym and envpool #257
Comments
EnvpoolC++#include <iostream>
#include "envpool/core/env.h"
#include "envpool/core/py_envpool.h"
#include "envpool/core/async_envpool.h"
using namespace std;
namespace cust {
class CustEnvFns {
public:
static decltype(auto) DefaultConfig() { return MakeDict(); }
template <typename Config>
static decltype(auto) StateSpec(const Config& conf) {
return MakeDict("obs"_.Bind(Spec<float>({-1})));
}
template <typename Config>
static decltype(auto) ActionSpec(const Config& conf) {
return MakeDict("action"_.Bind(Spec<int>({-1}, {0, 1})));
}
};
using CustEnvSpec = EnvSpec<CustEnvFns>;
class CustEnv : public Env<CustEnvSpec> {
protected:
bool done_{true};
float obs_{0.0F};
public:
CustEnv(const Spec& spec, int env_id) : Env<CustEnvSpec>(spec, env_id) {}
bool IsDone() override { return done_; }
void Reset() override {
obs_ = 0.0F;
done_ = false;
// debug message
cout << "env " << env_id_ << " reset." << endl;
WriteState(0.0);
}
void Step(const Action& action) override {
++obs_;
int act = action["action"_];
if (act == 1) {
done_ = true;
}
WriteState(1.0);
}
private:
void WriteState(float reward) {
auto state = Allocate();
state["reward"_] = reward;
state["obs"_] = obs_;
}
};
using CustEnvPool = AsyncEnvPool<CustEnv>;
} // namespace cust
using CustEnvSpec = PyEnvSpec<cust::CustEnvSpec>;
using CustEnvPool = PyEnvPool<cust::CustEnvPool>;
PYBIND11_MODULE(cust_envpool, m) { REGISTER(m, CustEnvSpec, CustEnvPool) }Pythonimport numpy as np
from envpool.python.api import py_env
from cust_envpool import _CustEnvSpec, _CustEnvPool
(
CustEnvSpec,
CustDMEnvPool,
CustGymEnvPool,
CustGymnasiumEnvPool,
) = py_env(_CustEnvSpec, _CustEnvPool)
if __name__ == "__main__":
print(" -- test envpool env -- ")
elapsed = 0
env_config = dict(zip(CustEnvSpec._config_keys,
CustEnvSpec._default_config_values))
env_config["num_envs"] = 1
vec_env = CustGymEnvPool(CustEnvSpec(tuple(env_config.values())))
obs, _ = vec_env.reset()
print(f"-*- Elapsed {elapsed} -*-")
print(obs)
elapsed += 1
# env 0 reset.
# -*- Elapsed 0 -*-
# [0.]
action = np.array([0])
obs, rew, terminate, truncate, info = vec_env.step(action)
print(f"-*- Elapsed {elapsed}, Action {action} -*-")
print(obs, terminate)
elapsed += 1
# -*- Elapsed 1, Action [0] -*-
# [1.] [False]
action = np.array([1])
obs, rew, terminate, truncate, info = vec_env.step(action)
print(f"-*- Elapsed {elapsed}, Action {action} -*-")
print(obs, terminate)
elapsed += 1
# -*- Elapsed 2, Action [1] -*-
# [2.] [ True]
action = np.array([0])
obs, rew, terminate, truncate, info = vec_env.step(action)
print(f"-*- Elapsed {elapsed}, Action {action} -*-")
print(obs, terminate)
elapsed += 1
# env 0 reset.
# -*- Elapsed 3, Action [0] -*-
# [0.] [False]
action = np.array([0])
obs, rew, terminate, truncate, info = vec_env.step(action)
print(f"-*- Elapsed {elapsed}, Action {action} -*-")
print(obs, terminate)
elapsed += 1
# -*- Elapsed 4, Action [0] -*-
# [1.] [False]Gymimport gym
import numpy as np
from gym import spaces
from gym.vector import SyncVectorEnv
class CustGymEnv(gym.Env):
def __init__(self, env_id) -> None:
self._env_id = env_id
self._done = False
self._obs = np.array([0.0], dtype=np.float32)
self.action_space = spaces.Discrete(2)
self.observation_space = spaces.Box(
np.array([-1.0], dtype=np.float32),
np.array([1.0], dtype=np.float32),
dtype=np.float32)
def step(self, action):
self._obs += 1.0
if action == 1:
self._done = True
return self._obs, 1.0, self._done, False, {}
def reset(self):
self._done = False
self._obs = np.array([0.0], dtype=np.float32)
print(f"env {self._env_id} reset")
return self._obs, {}
def render(self):
return None
def close(self):
return None
if __name__ == "__main__":
print(" -- test gym env -- ")
elapsed = 0
vec_env = SyncVectorEnv([lambda: CustGymEnv(0)])
obs, _ = vec_env.reset()
print(f"-*- Elapsed {elapsed} -*-")
print(obs)
elapsed += 1
# env 0 reset
# -*- Elapsed 0 -*-
# [[0.]]
action = np.array([0])
obs, rew, terminate, truncate, info = vec_env.step(action)
print(f"-*- Elapsed {elapsed}, Action {action} -*-")
print(obs, terminate)
elapsed += 1
# -*- Elapsed 1, Action [0] -*-
# [[1.]] [False]
action = np.array([1])
obs, rew, terminate, truncate, info = vec_env.step(action)
print(f"-*- Elapsed {elapsed}, Action {action} -*-")
print(obs, terminate)
elapsed += 1
# env 0 reset
# -*- Elapsed 2, Action [1] -*-
# [[0.]] [ True]
action = np.array([0])
obs, rew, terminate, truncate, info = vec_env.step(action)
print(f"-*- Elapsed {elapsed}, Action {action} -*-")
print(obs, terminate)
elapsed += 1
# -*- Elapsed 3, Action [0] -*-
# [[1.]] [False]
action = np.array([0])
obs, rew, terminate, truncate, info = vec_env.step(action)
print(f"-*- Elapsed {elapsed}, Action {action} -*-")
print(obs, terminate)
elapsed += 1
# -*- Elapsed 4, Action [0] -*-
# [[2.]] [False] |
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In short, auto-reset in gym: in envpool the auto-reset solution in envpool will result in two controlversial transitions when someone use env to collect transitions these two transitions, specially for the second, are slightly abnormal and not in logical(specially when model the state transition in model-based case) and should not be involed in ReplayBuffer. any suggestions for address these issues when some Collector follows the gym.vec.env auto-reset protocol? |
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The motivation is #194 (comment) for performance. Can we move this discussion to #194? |
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@Trinkle23897 I would like to learn about other issues related to reset. Can the automatic reset function be manually turned off? Because the environment that you do not want to end during evaluation is automatically reset |
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You can control env_id in env.step to decide which env needs to be stepped or not |
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@Trinkle23897 Can you help me write the key code that can specify the control environment? Because I couldn't find the code to control the specified environment. Do you use env. send (action, env_id) one by one for processing? |
def step(222 | self, |
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https://github.com/sail-sg/envpool/blob/main/envpool/atari/atari_pretrain_test.py this is the evaluation script |
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@Trinkle23897 Thanks |
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@Trinkle23897 This method can work normally. But this has great limitations. For example, if generating actions and environment execution cannot occur simultaneously. There will be situations where the execution of actions is inconsistent with the environment's ids. It makes it difficult to abstract the code every time you have to retrieve the latest ids from the info. Then, if you add functions such as skipping frames. It is possible to automatically reset during operation. Unable to accelerate while obtaining evaluation results and scores effectively. If there are any plans. Suggest a new version. Add the function to disable automatic environment reset |
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Sorry, I think it's hard by design |
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Okay, I have already planned to use the following logic. All environments are still running. When evaluating Set a first_ Done The state of not being done. Reward for accumulation. Once the firstdone score is reached, it also accumulates. But the scores did not increase afterwards. Because of parallel execution. It is possible that some environments may not have done it yet, while others may have done it twice or more. So we can only accumulate the scores before firstdone. Until all first-done states have completed the jump out loop I still hope there will be a feature to disable autoreset in the future |
Motivation
After reading the auto-reset document, I made some simple attempts between gym and envpool, noticed that the results given by envpool are different from those of gym.
In my test code, when action=1, terminated is set to true and obs is reset to 0. Otherwise, terminated is set to false and obs is incremented by 1 each time.
envpool:
gym(SyncVectorEnv):
What should I do to align the auto-reset functionality of envpool with that of gym?In my actual use sernario, I don't want to discard any actions because they are all meaningful.
Additional context
gym == 0.26.2
envpool == 0.8.1
numpy == 1.24.2
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